Journal of Advanced Research in Civil and Environmental Engineering

Optimizing Pedestrian Accessibility: A Comprehensive Analysis of Factors Influencing Walking Distances to Preferred Public Transport Bus Stops

2024-02-02T13:58:39+00:00

In urban planning, environmental sustainability, and transportation planning, the ability to walk to a bus stop is crucial. This study explores the complex relationship between walkability factors and societal influences on pedestrian behavior. It uses a multidisciplinary approach in recognition of the importance of public transportation nodes and the critical role that walkability plays in shaping towns and cities. Observational studies, questionnaire surveys are methods used to study pedestrian accessibility to public bus stops. The results showed complex interactions among environmental, social, and infrastructural elements and highlight how pedestrian decisions are dynamic and how transportation infrastructure affects community mobility. Age, gender, walking distance, education, trip purpose and cleanliness of bus stop have significant impact willingness to walk to nearest bus stop.

The willingness to walk in term of distance at present and future condition were studied it was observed that if the existing infrastructure improved they showed their willing to walk in average minimum 476.32 meter and 1670.27 meter in maximum. It was observed that the bus stop at rural area have greater service coverage area But in the case of the bus stops at urban area, buses users have more option in the case of selection of bus stop for trip. As a result, it was observed that urban bus have small service coverage area in the comparison the rural bus stop. The regression models were developed at 95% confidence level and it was observed to be statistically significant at 95% confidence level based on the R2 F-statistic. The findings, highlights the necessity of modified urban planning initiatives to improve walkability and maximize the layout of public transportation stops. Conclusively, this research provides significant perspectives to the domains of urban sociology and transportation studies, providing a comprehensive understanding of the societal factors influencing pedestrian behavior and the critical role of comprehensive bus stop accessibility assessments in sustainable urban development.

Nature-Based Solutions to Manage Storm Runoff in Urban Bengaluru – A Prelude

2024-03-22T11:29:47+00:00

Excess stormwater in urban areas leads to an emerging disaster like Urban floods. Urban flooding in recent times has received considerable attention globally due to its disastrous impacts owing to loss of human lives and damage to infrastructure. Urban flooding diverges from flash flooding by the fact that, with the advancements in urbanization, the urban flood increases from 1.8 to 8 times and flood volumes by up to six times. Rapid population growth and rampant urbanization over the years have encroached the existing water bodies resulting in a decrease in rainwater infiltration and increased runoff and flood rates.
This research paper examines the potential of nature-based solutions to manage storm runoff in urban Bengaluru. The study assesses the current state of stormwater management in the city and evaluates the effectiveness of different nature-based approaches, including green roofs, rain gardens, and permeable pavements. The results of the study demonstrate that nature-based solutions can significantly reduce the volume of stormwater runoff and improve water quality in urban areas. The study serves as a prelude to future research on the implementation and scalability of nature-based solutions for stormwater management in Bengaluru and other urban areas. Overall, this research suggests that nature-based solutions can provide a cost-effective and sustainable approach to manage storm runoff in Bengaluru and have the potential to improve the overall livability and resilience of the city.

Enhancing Success in Small Hydropower Projects: Analysis of Key Performance Indicators and Strategies for Improvement in Nepal

2024-04-15T11:46:59+00:00

Construction KPIs indicate project success. KPI measurements help implementers gather feedback for new projects. This study examines KPIs for small hydropower projects in Nepal, their ranking, the most important influencing element for each KPI, and how to improve KPI control in small hydropower projects in Nepal. Developers, consultants, and contractors were the respondents and project stakeholders. The data was summarised, analysed using SPSS and Excel data, and presented in the form of tables and figures. The stakeholders in small hydropower projects ranked the cost aspect first, and environmental management is the least significant KPI in small hydropower projects in Nepal. The hypothesis test showed that there are significant differences between the rankings given by the client, consultant, and contractor. The study presented the relative relevance index (RII) for each KPI component affecting Nepalese small hydropower projects.
Rework cost is the most important factor in KPI cost, while project cash flow is the least crucial. “Delay in contractor approval of updated programme” primarily affects KPI-Time, and the least important is site mobilisation and preparation delay. “Quality-related training and meetings” affected KPI-Quality the most, and specification conformity affected it the least. The ‘Number of new projects per year” component has the highest RII, and the least one is “mobilisation of necessary resources at site” in KPI productivity. For KPI-client satisfaction, “number of reworks and speed and reliability of service to owner” is most important, and information synchronisation between owner and project partners has the least impact.

Machine Learning Approaches for Predicting Concrete Compressive Strength

2024-05-04T11:07:01+00:00

Concrete compressive strength (CS) plays a crucial role in infrastructure development. Accurate and timely prediction of compressive strength is crucial for optimising the performance of structural components. In this study, 776 experimental datasets were collected from past research. These datasets were analysed with different machine learning (ML) techniques. The study evaluated the applicability of ML approaches in forecasting concrete strength. The forecasted performance of the regression model was compared with different statistical parameters. In this study, output performance revealed that the random forest (RF) regression model has good CS prediction capabilities by its R-squared value of 0.91 followed by k-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DT) with 0.88, 0.84, and 0.78 respectively. Therefore, this research establishes that the ML approach has a good capacity to forecast the concrete CS based on the real database. These predictions approach plays perfect integration into the construction industry to timely prediction of CS of concrete with high precision and efficiency.